Content-based routing and rating of messages in a telecommunications network

ABSTRACT

Systems and methods for automated routing and rating of communication data.

CROSS-REFERENCE

This application is a continuation of U.S. patent application Ser. No.15/994,644, filed on May 31, 2018, now U.S. Pat. No. 10,798,534, whichclaims priority to U.S. Provisional Patent App. No. 62/513,241, filed onMay 31, 2017. The contents of each application are incorporated hereinby reference in their entirety.

TECHNICAL FIELD

This disclosure relates to the field of telecommunications, and moreparticularly, to routing and rating messages based on determinedclasses.

BACKGROUND

Telecommunications carriers, intercarrier providers, content providers,retail services providers, and other stakeholders (sometimescollectively referred to herein as carriers or service providers)currently face many challenges affecting revenues and operational costs.A particular challenge exists with respect to the routing and rating ofmessages. Some carriers may only wish to allow messages of certainclasses to be transmitted. Further, carriers may rate messagesdifferently based on class. But improper or misclassified messages cancause carriers to lose messages, transmit messages improperly, and loserevenue. Improper and misclassified messages also create the potentialfor undesirable messaging reaching end users, and may also create datasecurity and privacy concerns.

Existing network architecture does not provide carriers the ability toadequately account for mislabeled, misclassified, or misidentifiedmessages when rating the messages. Nor does it provide the ability tomonitor and verify message classification on a scale present in today'scommunications environment.

The invention(s) described herein is/are directed, but not limited, toaddressing these and other issues associated with existing systems andmethods. Other aspects and benefits not related to these issues arecontemplated as well.

SUMMARY

Systems and methods for automatic routing and rating of messages areprovided. According to a particular and non-limiting aspect, an examplesystem may include a computing device configured to receive from asource element, a message destined for a telecommunications networkentity, such as a commercial mobile radio service (CMRS) entity, whereinthe message comprises a service category. The service category may be apredetermined category decided by the source element, defining thecontent, purpose, or other characteristic of the message. The servicecategory may be arbitrarily set, and may not be related to the actual ortrue classification of the message.

The computing device may receive the message, and may determine whetheror not to classify the message. Some messages may not requireclassification, such as those sent by a trusted source. These messagesmay be passed on to their appropriate destinations.

Messages that do require classification, however, may be classified bythe computing device. The computing device may apply a deep learningalgorithm to the message to determine the class. The deep learningalgorithm may be generated, created, or determined by receiving andanalyzing many messages (e.g., thousands, millions, or more). The deeplearning algorithm may run an iterative process to update, modify, orotherwise adapt, considering each message payload, contents, source,destination, time stamps associated with one or more aspects of themessage transmission, and other message characteristics.

After determining the message class, the computing device may comparethe message class to the service category (if any) provided by thesource element. This comparison may allow the computing device todetermine whether the message was mislabeled, which can affect atrustworthiness rating of the source element.

The computing device may then determine a rating for the message, anddeliver the message to the destination.

The process described above (and elsewhere in this disclosure) may beperformed for many messages over a period of time, such that atrustworthiness of a given source element can be determined and updated.In turn, the rating applied to one or more messages may be modifiedbased on a trustworthiness of the source element of the particularmessage, such that untrustworthy source elements may have a ratingmultiplier applied to their messages. Other actions can be taken aswell.

Further, some embodiments may be described herein as including receivinga message, classifying a message, rating a message, and delivering amessage. But other embodiments may include fewer ore more steps, such asincluding receiving, classifying and rating a message (i.e., withoutdelivering the message). Other combinations of steps, functions, andactions are contemplated as well.

These and other aspects will become readily apparent from the writtenspecification, drawings, and claims provided herein.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram of an example system according toembodiments of the present disclosure.

FIG. 2 illustrates example messages, service categories, andclassifications according to embodiments of the present disclosure.

FIG. 3 is a schematic diagram of an exemplary computing device capableof supporting and facilitating one or more aspects described herein.

FIG. 4 is a flowchart illustrating an example method according toembodiments of the present disclosure.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

The description that follows describes, illustrates and exemplifies oneor more particular embodiments of the invention(s) in accordance withits principles. This description is not provided to limit theinvention(s) to the embodiments described herein, but rather to explainand teach the principles of the invention(s) in such a way to enable oneof ordinary skill in the art to understand these principles and, withthat understanding, be able to apply them to practice not only theembodiments described herein, but also other embodiments that may cometo mind in accordance with these principles. The scope of theinvention(s) is/are intended to cover all such embodiments that may fallwithin the scope of the appended claims, either literally or under thedoctrine of equivalents.

It should be noted that in the description and drawings, like orsubstantially similar elements may be labeled with the same referencenumerals. However, sometimes these elements may be labeled withdiffering numbers, such as, for example, in cases where such labelingfacilitates the didactic purpose of the specification. Additionally, thedrawings set forth herein are not necessarily drawn to scale, and insome instances proportions may have been exaggerated to more clearlydepict certain features. Such labeling and drawing practices do notnecessarily implicate an underlying substantive purpose. Furthermore,one or more drawings herein may be of a purely functional schematicnature, or a combination of a functional and structural/hardwareschematic nature, intended to disclose and teach functional aspects ofthe subject matter without necessarily limiting the disclosure to anyspecific structure/hardware. As stated above, the present specificationis intended to be taken as a whole and interpreted in accordance withthe principles of the invention(s) as taught herein and understood toone of ordinary skill in the art.

With respect to the exemplary systems, components and architecturedescribed and illustrated herein, it should also be understood that theinvention(s) may be embodied by, or employed in, numerous configurationsand components, including one or more system, hardware, software, orfirmware configurations or components, or any combination thereof, asunderstood by one of ordinary skill in the art. Accordingly, while thedrawings illustrate exemplary systems including components for one ormore of the embodiments contemplated herein, it should be understoodthat with respect to each embodiment, one or more components may not bepresent or necessary in the system. Furthermore, although one or moresystems, devices, apparatuses, and other components may be described,all methods, systems, and articles of manufacture consistent with knownarchitecture for these components are intended to be encompassed. Forexample, a processor may be implemented as part of one or morecomponents as a microprocessor, microcontroller, application specificintegrated circuit (ASIC), discrete logic, or a combination of othertype of circuits or logic. Similarly, memories as part of one or more ofthese components may be DRAM, SRAM, Flash or any other type of memory.Flags, data, databases, tables, and other data structures may beseparately stored and managed, may be incorporated into a single memoryor database, may be distributed, or may be logically and physicallyorganized in many different ways. Software programs, which may bedescribed in terms of one or more code segments, may be parts of asingle program, separate programs, or distributed across severalmemories and processors. The methods and functionality described hereinmay be implemented via hardware, software, and/or firmware, andprocessed by one or more processor-based systems, components or devices.Such methods and functionality may be described as a module or enginewith the understanding that its implementation is not limited to anyparticular hardware, software, firmware, or device configuration, butrather encompassing all implementations/embodiments within the skill ofone of ordinary skill in the art. Systems may be implemented inhardware, software, or a combination of hardware and software in oneprocessing system or distributed across multiple processing systems.Accordingly, the invention(s) should not be construed as limited by theexemplary embodiments described herein or any of the associated didacticschematics.

The systems and associated methods described herein facilitate automaticrouting and rating of messages. Over-The-Top (OTT) telecommunicationsentities (as well as other entities such as marketing, business, orother Application to Person (A2P) based entities) often desire todeliver messaging traffic to the subscriber base of one or morecarriers. Carriers may route and/or rate messages based on theircontent, such as by charging more for advertising messages than forordinary person-to-person communications. Further, carriers may wish toprevent certain classes of messages from being transmitted to theirdestinations at all, such as spam, or other messages associated withrisk of fraud, malware or breach of data privacy/security.

Some communication structures may use a message service category toroute and rate messages. This service category may be assigned by thesource element (i.e., the OTT entity), and may thus not always beaccurate. OTT entities may be incentivized to classify messages inservice categories that cost less and/or will not be flagged as spam orotherwise prevented from reaching their destination.

Embodiments of the present disclosure may enable one or more parties toindependently determine the message classification, such that theappropriate routing and rating may occur. Further, based on theindependent classification, source elements or OTT entities can bedeemed trustworthy or not, and may be given an associated ranking,grade, or other score based on the accuracy with which they label theservice categories of messages. This ranking may then determine a ratingmultiplier, such that messages sourced from untrustworthy sourceelements are charged more than those from trusted source elements.

Thus, embodiments described herein may enable carriers to appropriatelyclassify or reconcile the originator categorization of messages, toavoid revenue leakage due to under charging or under-rating messages,and to avoid transmitting unauthorized or unwanted messages todestination elements (consumers).

FIG. 1 illustrates an exemplary, non-limiting embodiment of a system 100according to embodiments of the present disclosure. System 100 mayinclude source element 110, routing element 120, validation element 130,rating element 140, and destination element 150. One or more of theelements of system 100 may part of a computing system or computingdevice, such as the computing device described with respect to FIG. 3.

Source element 110 may be a message source, configured to generate,originate, or otherwise create one or more messages to be transmitted todestination element 150 via one or more network paths. In some examples,source element 110 may be any OTT entity, such as Twillio, Bandwidth,MessageBird, Plivo, and other communication entities. Source element 110may be any non-CMRS (commercial mobile radio service) entity.

Source element 110 (e.g., an OTT entity) may desire to transmit amessage to one or more destination elements 150. Destination element 150may include any CMRS entity, including the subscriber bases of entitiessuch as AT&T, Verizon, Sprint, T-Mobile, Vodafone, and others. Theseentities may provide a network path to their subscribers over which theOTT entities wish to communicate. Destination element 150 may also be anon-CMRS entity, such as another OTT entity.

In some examples, each message transmitted by source element 110 mayhave a corresponding time stamp, payload, IP address, source elementidentity or carrier identity, destination element identity, and/or othercharacteristics. Further, each message may have a corresponding servicecategory.

In some examples, the source element 110 may supply the service categoryto each message. This may occur, for instance, by the source elementpopulating the service category in a “tag-length value” or “type-lengthvalue” (TLV) of the message. Some messages may take the form of SMPPmessages, for which the TLV may be a field. Alternatively, the systems,devices, and methods described herein may be leveraged in connectionwith any type of communications session, and may involve, withoutlimitation, SMS or SMS like messages, MIMS messages, video calls, videostreams, VoIP voice calls, HD VoIP voice calls, VoLTE voice calls, HDVoLTE voice calls, VoWi-Fi voice calls, application push notifications,and the like.

Example message service categories may include (1) person to person(P2P) communication, (2) application to person (A2P) communication, and(3) SPAM. A2P communication may further include or be broken down into(i) notification messages (e.g., marketing, service, or financial), (ii)activation messages (e.g., two-factor authentication and serviceactivation), (iii) validation messages, (iv) marketing messages (e.g.,promotions or lead generation messages), and (v) voting or pollingmessages. Other categories may be included as well. FIG. 2 illustratesexample messages and corresponding service categories.

Routing element 120 may be configured to receive the message(s)transmitted by source element 110. Routing element 120 may then beconfigured to make one or more determinations regarding the message,such as whether or not classification, or reconciliation of theclassification of the message is required.

Determining that classification is required may include determiningwhether the message already has a corresponding service category. Whererouting element 120 receives a message without a corresponding servicecategory, routing element 120 may determine that classification of themessage is required. Further, even where the message already has acorresponding service category, routing element 120 may still determinethat classification of the message is required.

Determining whether to classify the message may be done based on thesource connection, source telecommunications network, or othercharacteristics of the message and/or source of the message.

If classification is not required, the message may be passed along tothe appropriate destination element 150. However, if classification isrequired, the message (and/or information, data, or othercharacteristics of the message) may be transmitted to the validationelement 130.

Validation element 130 may receive messages, message data, or othermessage characteristics from routing element 120. Validation element 130may also perform an analysis of the message, and transmit aclassification of the message back to routing element 130.

In some examples, validation element 130 may evaluate the receivedmessage based on one or more characteristics, such as the messagepayload (i.e., contents), source address, destination address, and/ortime stamps associated the message. Then, based on a deep learningalgorithm, validation element 130 may determine a classification of themessage. The classification may include one or more of the samecategories described above with reference to the service category, withexamples shown in FIG. 2.

In some examples, the deep learning algorithm may be an algorithmdetermined initially by feeding a plurality of messages, servicecategories, classifications, and other associated information into analgorithmic process. It may then involve processing the messages, anditeratively updating the algorithm.

The deep learning algorithm may include leveraging Word2Vec modeling toincrease classification accuracy and speed via word clustering. Word2Vecmodeling may include converting each word or group of words of a messageinto vectors which can be added, subtracted, or otherwise manipulated invector space.

The deep learning algorithm may also leverage algorithms and decisionmaking structures configured to process images, sounds, videos, andother types of messages in addition to text, such as those describedherein.

The classification of a given message may be determined based on thedeep learning algorithm and various message characteristics such as thepayload, time stamp, source address, destination address, source elementidentity, destination element identity, and other factors. For instance,the message payload may play a central role in determining the messageclassification. The payload, or contents, of the message may inform thepurpose of the message. Further, when a plurality of messages areanalyzed, the payload of all the messages may be analyzed to determinepatterns and how the patterns change over time. This can be useful indetermining a classification for a particular message.

One or more time stamps associated with the message can relate to apoint in time at which the message was sent, the point(s) in time atwhich the message reached various nodes or intermediate points along itsroute, and the message “velocity.” A message velocity may refer to thespeed at which a particular source entity is sending messages. Forinstance, the velocity may be higher for a first source entity sendingmore messages in a given time period than a second source entity thatonly sends a few. The message corresponding to the first source entitymay thus have a corresponding high velocity. Messages with a highvelocity, or velocity greater than a certain threshold, may be deemedSPAM or categorized as A2P messages. The time stamps of many messagesmay be analyzed together, to provide additional information about groupsof messages and/or particular source elements. For instance, a givensource element may transmit many hundreds or thousands of messages atnearly the same time, which may be detected and used to classify one ormore messages as SPAM or types of A2P messaging.

Similarly, a message source address can inform whether the message isSPAM or some other classification. A message sent from a given sourceelement notorious for sending SPAM may be classified as SPAM morereadily, or may be more heavily scrutinized or otherwise analyzeddifferently than a message from a trusted source element. As, theidentity of the source element can play an important role in determiningthe classification for a given message. The deep learning algorithm mayfurther consider one or more other message characteristics in making theclassification.

Once a classification has been made, validation element 130 may transmitthe classification to routing element 120. Routing element 120 may thenresponsively generate and/or transmit a record, such as a call detailrecord (CDR) to the rating element 140. The CDR may include informationcorresponding to the message, such as one or more parties involved,various time stamps, identification information, service categories andclassifications, a route taken by the message through one or morenetworks, any fault conditions encountered, and more. In someembodiments, the CDR may be called a Message Detail Record (MDR) or bysome other term.

Rating element 140 may be configured to receive the record andresponsively assign a rating to the message. The message rating may bebased on one or more factors, including the record (e.g., CDR), theservice category, the classification, the rating structure determined bythe particular carrier network over which the message is beingtransmitted, the status of the message, type of message, trustworthinessor other grade associated with the source element and/or message, thedestination element, and/or various other factors. After a rating isdecided, the message may then be transmitted by the routing element 120to the destination element 150.

FIG. 3 illustrates a simplified block diagram of an example computingdevice 300 according to embodiments of the present disclosure. Computingdevice 300 may be configured for performing a variety of functions oracts, such as those described in this disclosure (and accompanyingdrawings). The computing device 300 may include various components,including for example, a processor 310, memory 320, user interface 330,and communication interface 340, all communicatively coupled by systembus, network, or other connection mechanism 350. It should be understoodthat examples disclosed herein may refer to computing devices and/orsystems having components that may or may not be physically located inproximity to each other. Certain embodiments may take the form of cloudbased systems or devices, and the term “computing device” should beunderstood to include distributed systems and devices (such as thosebased on the cloud), as well as software, firmware, and other componentsconfigured to carry out one or more of the functions described herein.

Processor 310 may include a general purpose processor (e.g., amicroprocessor) and/or a special purpose processor (e.g., a digitalsignal processor (DSP)). Processor 310 may be any custom made orcommercially available processor, such as, for example, a Core series orvPro processor made by Intel Corporation, or a Phenom, Athlon or Sempronprocessor made by Advanced Micro Devices, Inc. In the case where thecomputing device 300 is a server, the processor 310 may be, for example,a Xeon or Itanium processor from Intel, or an Opteron-series processorfrom Advanced Micro Devices, Inc. Processor 310 may also representmultiple parallel or distributed processors working in unison.

Memory 320 may include one or more volatile (e.g., random access memory(RAM, such as DRAM, SRAM, SDRAM, etc.)) and nonvolatile (e.g., ROM, harddrive, flash drive, CDROM, etc.), removable, and/or non-removablestorage components, such as magnetic, optical, or flash storage, and maybe integrated in whole or in part with the processor 310. These andother components may reside on devices located elsewhere on a network orin a cloud arrangement. Further, the memory 320 may take the form of anon-transitory computer-readable storage medium, having stored thereonprogram instructions (e.g., compiled or non-compiled program logicand/or machine code) that, when executed by the processor 310, cause thedevice 300 to perform one or more functions or acts, such as thosedescribed in this disclosure. Such program instructions may define or bepart of a discrete software application that can be executed in responseto certain inputs received from the user interface 330 and/orcommunication interface 340, for instance. Memory 320 may also storeother types of information or data, such as those types describedthroughout this disclosure.

User interface 330 may facilitate interaction with a user of the device,if applicable. As such, user interface 330 may include input componentssuch as a keyboard, a keypad, a mouse, a touch-sensitive panel, amicrophone, and a camera, and output components such as a display screen(which, for example, may be combined with a touch-sensitive panel), asound speaker, and a haptic feedback system. The user interface 330 mayalso comprise devices that communicate with inputs or outputs, such as ashort-range transceiver (RFID, Bluetooth, etc.), a telephonic interface,a cellular communication port, a router, or other types of networkcommunication equipment. The user interface 330 may be internal to thecomputing device 300, or may be external and connected wirelessly or viaconnection cable, such as through a universal serial bus port.

Communication interface 340 may be configured to allow the device 300 tocommunicate with one or more devices (or systems) according to one ormore protocols. In one example, the communication interface 340 may be awired interface, such as an Ethernet interface or a high-definitionserial-digital-interface (HD-SDI). As another example, the communicationinterface 340 may be a wireless interface, such as a cellular or WI-FIinterface. In some examples, each of a plurality of computing devices300 and/or other devices or systems on a network may be configured touse the Internet protocol suite (TCP/IP) to communicate with oneanother. It will be understood, however, that a variety of networkprotocols could also be employed, such as IEEE 802.11 Wi-Fi, addressresolution protocol ARP, spanning-tree protocol STP, orfiber-distributed data interface FDDI. It will also be understood thatwhile some embodiments may include computing device 300 having abroadband or wireless connection to the Internet (such as DSL, Cable,Wireless, T-1, T-3, OC3 or satellite, etc.), the principles of theinvention are also practicable with a dialup connection through astandard modem or other connection means. Wireless network connectionsare also contemplated, such as wireless Ethernet, satellite, infrared,radio frequency, Bluetooth, near field communication, and cellularnetworks.

In the context of this document, a “computer-readable medium” may be anymeans that can store, communicate, propagate, or transport data objectsfor use by or in connection with the systems and methods as describedherein. The computer readable medium may be for example, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, device, propagation medium, or any other device with similarfunctionality. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: an electricalconnection (electronic) having one or more wires, a random access memory(RAM) (electronic), a read-only memory (ROM) (electronic), an erasableprogrammable read-only memory (EPROM, EEPROM, or Flash memory)(electronic), an optical fiber (optical), and a portable compact discread-only memory (CDROM) (optical). Note that the computer-readablemedium could even be paper or another suitable medium upon which theprogram is printed, as the program can be electronically captured, via,for instance, optical scanning of the paper or other medium, thencompiled, interpreted or otherwise processed in a suitable manner ifnecessary, and stored in a computer memory. The systems, devices, andmethods can be embodied in any type of computer-readable medium for useby or in connection with an instruction execution system or apparatus,such as a computer.

FIG. 4 illustrates an example method 400 according to embodiments of thepresent disclosure. Method 400 may be performed by one or more computingdevices or systems, such as computing device 300 described with respectto FIG. 3. The flowchart of FIG. 4 is representative of machine readableinstructions that are stored in memory (such as memory 320 of computingdevice 300) and may include one or more programs which, when executed bya processor (such as processor 310 of computing device 300) may causeone or more systems or devices to carry out one or more functionsdescribed herein. While the example program is described with referenceto the flowchart illustrated in FIG. 4, many other methods for carryingout the functions described herein may alternatively be used. Forexample, the order of execution of the blocks may be rearranged orperformed in series or parallel with each other, blocks may be changed,eliminated, and/or combined to perform method 400. Further, becausemethod 400 is disclosed in connection with the components of FIGS. 1-3,some functions of those components will not be described in detailbelow.

Method 400 may start at block 400. At block 404, method 400 may includeinputting messages, service categories, and/or message classificationsto one or more computing devices or systems. Block 406 may then includedeveloping the deep learning algorithm based on the input.

At block 408, method 400 may include receiving a message. The messagemay be received by a routing element from a source element. The sourceelement may append, add, or otherwise populate a field of the messagewith a service category describing one or more aspects of the message.At block 410, method 400 may include determining whether or not toclassify the received message.

If it is determined to classify the message, method 400 may includeblock 412 in which the message classification is determined. Thisdetermination may be performed based on the deep learning algorithm,which may factor in message characteristics such as the message payload,time stamps, source address and destination address.

And if it is determined to not classify the message, method 400 mayproceed to block 414 wherein a record is transmitted to the ratingelement. The rating element then may determine a rating for the messageat block 416. The rating may be determined based on one or morecharacteristics of the message, including the initial service category(and whether it differs from a determined classification), a sourceidentity and/or source score, ranking, or other evaluation, and/or oneor more other factors.

At block 418, method 400 may include transmitting the message to theappropriate destination. Method 400 may then end at block 420.

In some examples, the rating and associated methods described herein canbe used in connection with management and financial concerns betweenvarious entities. For example, a business rules engine (not shown) maybe deployed in a system, which provides the ability for customizedconfiguration of how the system processes and treats content trafficbased on ratings and takes into consideration relationships betweenvarious networks and carriers. Specific rules attributable to variousentities involved, such as carrier(s), content provider(s), or othernetwork(s) can be applied. Clearing and reporting capabilities tofacilitate financial settlement of content traffic and delivery are alsocontemplated.

While one or more specific embodiments have been illustrated anddescribed in connection with the invention(s), it is understood that theinvention(s) should not be limited to any single embodiment, but ratherconstrued in breadth and scope in accordance with recitation of theappended claims.

What is claimed is:
 1. A system for automated routing and rating of communications data comprising: a computing device configured to: receive from a source element, a message destined for a destination element, wherein the message comprises a service category; determine whether to classify the message; determine a message class, wherein determining the message class comprises: applying, to the message, a deep learning algorithm generated based on a plurality of messages each having a payload, time stamp, source address, and destination address; and determining the message class based on the deep learning algorithm; compare the service category to the determined message class; determine a rating for the message based on the comparison; and deliver the message to the destination element.
 2. The system of claim 1, wherein the destination element is associated with a commercial mobile radio service (CMRS) entity.
 3. The system of claim 1, wherein the source element is associated with an over-the-top (OTT) entity.
 4. The system of claim 1, wherein the deep learning algorithm utilizes at least one of the message payload, time stamp, source address, and destination address associated with the message to determine the message class.
 5. The system of claim 1, wherein the deep learning algorithm utilizes a message velocity to determine the message class.
 6. The system of claim 1, wherein the message class comprises one of a person to person (P2P) communication; an application to person (A2P) communication; and SPAM.
 7. The system of claim 6, wherein the A2P communication comprises one of a notification message; an activation message; a validation message; a marketing message; and a voting message.
 8. A system for automated routing and rating of communications data comprising: a computing device configured to: receive a message from a source element, wherein the message comprises a service category; determine whether to classify the message; determine a message class, wherein determining the message class comprises: applying, to the message, a deep learning algorithm generated based on a plurality of messages having respective payloads, time stamps, source addresses, and destination addresses; and determining the message class based on the deep learning algorithm; compare the service category to the determined message class; and determine a rating for the message based on the comparison.
 9. The system of claim 8, wherein the source element is associated with an over-the-top (OTT) entity.
 10. The system of claim 8, wherein the deep learning algorithm utilizes at least one of the message payload, time stamp, source address, and destination address associated with the message to determine the message class.
 11. The system of claim 8, wherein the deep learning algorithm utilizes a message velocity to determine the message class.
 12. The system of claim 8, wherein the message class comprises one of a person to person (P2P) communication; an application to person (A2P) communication; and SPAM.
 13. The system of claim 12, wherein the A2P communication comprises one of a notification message; an activation message; a validation message; a marketing message; and a voting message.
 14. The system of claim 8, wherein the rating comprises a numerical value.
 15. The system of claim 8, wherein the rating is determined based on a comparison of one or more of an initial service category; a source identity; a source score; and a ranking.
 16. A method for automated routing and rating of communications data via a computing device within a telecommunications network, the method comprising the steps of: at the computing device, receiving from a source element, a message destined for a destination element, wherein the message comprises a service category; determining at the computing device whether to classify the message; determining at the computing device a message class, wherein determining the message class comprises: applying, to the message, a deep learning algorithm generated based on a plurality of messages each having a payload, time stamp, source address, and destination address; and determining the message class based on the deep learning algorithm; comparing at the computing device the service category to the determined message class; determining at the computing device a rating for the message based on the comparison; and delivering the message to the destination element.
 17. The method of claim 16, wherein the destination element is associated with a commercial mobile radio service (CMRS) entity.
 18. The method of claim 16, wherein the source element is associated with an over-the-top (OTT) entity.
 19. The method of claim 16, wherein the deep learning algorithm utilizes at least one of the message payload, time stamp, source address, and destination address associated with the message to determine the message class.
 20. The method of claim 16, wherein the deep learning algorithm utilizes a message velocity to determine the message class. 